Project description:Regulatory proteins associate with the genome either by directly binding cognate DNA motifs or via protein-protein interactions with other regulators. Each genomic recruitment mechanism may be associated with distinct motifs, and may also result in distinct characteristic patterns in high-resolution protein-DNA binding assays. For example, the ChIP-exo protocol precisely characterizes protein-DNA crosslinking patterns by combining chromatin immunoprecipitation (ChIP) with 5’ to 3’ exonuclease digestion. Since different regulatory complexes will result in different protein-DNA crosslinking signatures, analysis of ChIP-exo sequencing tag patterns should enable detection of multiple protein-DNA binding modes for a given regulatory protein. However, current ChIP-exo analysis methods either treat all binding events as being of a uniform type, or rely on the presence of DNA motifs to cluster binding events into subtypes. To systematically detect multiple protein-DNA interaction modes in a single ChIP-exo experiment, we introduce the ChIP-exo mixture model (ChExMix). ChExMix probabilistically models the genomic locations and subtype membership of protein-DNA binding events using both ChIP-exo tag enrichment patterns and DNA sequence information, thus offering a principled and robust approach to characterizing binding subtypes in ChIP-exo data. We demonstrate that ChExMix achieves accurate detection and classification of binding event subtypes using in silico mixed ChIP-exo data. We further demonstrate the unique analysis abilities of ChExMix using a collection of ChIP-exo experiments that profile the binding of key transcription factors in MCF-7 cells. In these data, ChExMix detects cooperative binding interactions between FoxA1, ERalpha, and CTCF, thus demonstrating that ChExMix can effectively stratify ChIP-exo binding events into biologically meaningful subtypes.
Project description:The aim of this project was to identify microRNAs associated with the expression subtypes in patients diagnosed with non-small cell lung cancer. Based on mRNA expression data the samples were assign to a gene expression subtype based on 506 gene centroid classifier. The patients enrolled in this study underwent curatively intended surgical resection. Subtype-specific microRNA expression was correlated to gene set enrichment scores in order to identify associated pathways that the microRNAs may be regulating, characterizing the expression subtypes. In order to assess if the subtype-specific microRNA signal originate from the lung cancer cells or from infiltrating immune cells, we compared our subtype-specific microRNAs with the results from a study investigating human cell-specific microRNA expression. In this project the authors sequenced microRNAs from 46 primary cell types, 42 cancer cell lines and tissues (McCall MN, Kim MS, Adil M, Patil AH, Lu Y, Mitchell CJ, Leal-Rojas P, Xu J, Kumar M, Dawson VL et al: Toward the human cellular microRNAome. Genome Res 2017, 27(10):1769-1781). The results were further validated in independent NSCLC cohorts (LUAD and LUSC) from The Cancer Genome Atlas (TCGA). In this study we identified subtype specific microRNAs associated with essential processes characterizing the expression subtypes of ADs and SCCs.
Project description:Mapping ultra high resolution of Brachyury:DNA interaction would provide us with valuable new mechanistic insights into complex molecular transactions at Brachyury-bound enhancers. Embryonic stem cells were differentiated into Brachyury-positive mesoendoderm cells. And, ChIP-exo experiment was then performed to identify detailed Brachyury-DNA binding profiles.
Project description:The emerging evidences support that exosome cargo miRNAs function as important regulators in cell differentiation. Therefore, in order to figure out the mechanism that Exo-AT mediated adipogenesis, we profiled miRNAs in Exo-AT using high-throughput sequencing (miRNA-seq). After trimming low-quality reads, contaminants, adaptors, and reads smaller than 15 nt, the remaining reads were mapped to merged pre-miRNA data bases. To identify the conserved miRNAs in Exo exosomes, miRNAs were aligned to miRBase v21. 148 and 154 types of known miRNAs in Exo-ADSCs and Exo-AT, respectively, were identified in the two replicates. Among these miRNAs, 103 miRNAs were simultaneously detected in both Exo-ADSCs and Exo-AT. Compared to Exo-ADSCs, 45 conserved miRNAs were enriched (expressed ≥ 2 folds, FDR<0.05) in Exo-AT. KEGG Pathway analysis was performed for the targets of the most 20 enriched miRNAs in Exo-AT (compared with Exo-ADSCs) to determine their potential function. Data showed that pathways that regulate adipogenesis such as Wnt signaling pathway, Insulin signaling pathway, MAPK signaling pathway, TGF-ß signaling pathway were enriched significantly for targets of Exo-AT miRNAs. Furthermore, 14 of 45 enriched miRNAs in Exo-AT (31.11%, such as miR-30a-5p, miR-148a-3p) were reported to participate in regulation of adipogenesis while 8 miRNAs (17.78%, such as miR-93-5p, miR-150-3p) that negatively control osteoblastic differentiation of MSC have been described.
Project description:To explore the possibility of miRNA(s) contributing to the cardioprotection induced by plasma exosomes at the late phase of RIPC, we performed a miRNA profiling assay (763 rat miRNAs) comparing the differences between RIPC-exo and Control-exo using Illumina HiSeq 2500 high-throughput sequencing.
Project description:Chromatin immunoprecipitation followed by deep sequencing (ChIP-seq) and its ultra-high resolution cousin ChIP-exo are methods that identify where proteins bind along any genome in vivo. ChIP-exo achieves near-base pair resolution by creating exonuclease stop sites just 5’ to where formaldehyde-induced protein-DNA cross-links occur. Whereas construction of ChIP genomic libraries is straightforward and widely adopted for ChIP-seq, ChIP-exo is technically more involved which has resulted in limited adoption. Here we describe multiple ChIP-exo protocols, each with use-specific advantages and limitations. The new versions are greatly simplified through removal of multiple enzymatic steps. This is achieved in part through the use of Tn5 tagmentation and/or single-stranded DNA ligation. The result is greater library yields, lower processing time, and lower cost. A similar streamlined approach was developed for ChIP-seq, called ChIP-seq 1-step, where library construction is achieved in one-step.